OntoGene in BioCreative II.5

被引：34

作者：

Rinaldi, Fabio ^{[1
]}

Schneider, Gerold ^{[1
]}

Kaljurand, Kaarel ^{[1
]}

Clematide, Simon ^{[1
]}

Vachon, Therese ^{[2
]}

Romacker, Martin ^{[2
]}

机构：

[1] Univ Zurich, Inst Computat Linguist, CH-8050 Zurich, Switzerland

[2] Novartis Pharma AG, NIBR NITAS Text Min Serv, CH-4002 Basel, Switzerland

来源：

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS | 2010年 / 7卷 / 03期

基金：

瑞士国家科学基金会;

关键词：

Biomedical text mining; Natural Language Processing (NLP); protein interactions; BioCreative; EXTRACTION; CORPUS; MODELS; GENE;

D O I：

10.1109/TCBB.2010.50

中图分类号：

Q5 [生物化学];

学科分类号：

070307 [化学生物学];

摘要：

We describe a system for the detection of mentions of protein-protein interactions in the biomedical scientific literature. The original system was developed as a part of the OntoGene project, which focuses on using advanced computational linguistic techniques for text mining applications in the biomedical domain. In this paper, we focus in particular on the participation to the BioCreative II.5 challenge, where the OntoGene system achieved best-ranked results. Additionally, we describe a feature-analysis experiment performed after the challenge, which shows the unexpected result that one single feature alone performs better than the combination of features used in the challenge.

引用

页码：472 / 480

页数：9

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